A comparison of rule-based versus exemplar-based categorization using the ACT-R architecture

Abstract

A rule-based approach to categorization is compared with an exemplar-based approach. Both models were developed using the ACT-R architecture. Both approaches yield similar accuracy and are relatively impervious to varying model parameters. Implications for the nature of implicit and explicit knowledge and learning are discussed.